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Automated employee evaluation using fuzzy and neural network synergism through IoT assistance

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Abstract

In today’s dynamic environment, an appropriate performance evaluation method for industries is a complex problem considering its funding scale. Performance evaluation in present industries has become a key part of the strategic approach. Existing performance evaluation approaches are based on manual estimations. These are prone to bias and nepotism, and hence, these manual evaluation schemes may demotivate the employees. In order make this evaluation solely performance oriented, the authors have proposed a neuro-fuzzy-based framework. For detecting and tracking the employee activities, Internet of things (IoT)–enabled sensors are used, while artificial neural fuzzy inference system (ANFIS) is used for learning and automated decision optimization. With an accuracy rate of 94.7% and an estimated RMSE error value of 0.0717, the proposed framework is fit for adaption in any real-life industrial scenario.

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Authors and Affiliations

  1. Guru Nanak Dev University, Amritsar, Punjab, India

    Keshav Dhir & Amit Chhabra

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  1. Keshav Dhir
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  2. Amit Chhabra
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Correspondence to Keshav Dhir.

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Dhir, K., Chhabra, A. Automated employee evaluation using fuzzy and neural network synergism through IoT assistance. Pers Ubiquit Comput 23, 43–52 (2019). https://doi.org/10.1007/s00779-018-1186-6

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  • DOI: https://doi.org/10.1007/s00779-018-1186-6

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